STAM-LSGRU:用于短期预报的带边缘计算的时空雷达回波外推算法

Hailang Cheng, Mengmeng Cui, Yuzhe Shi
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引用次数: 0

摘要

随着移动边缘计算(MEC)的出现,将数据处理从云中心转移到网络边缘,为解决延迟敏感型应用提供了一种先进的计算模式。具体来说,在雷达系统中,雷达回波数据的实时处理和预测在动态和资源受限的环境中构成了重大挑战。MEC 通过在数据源附近处理数据,不仅能显著减少通信延迟,提高带宽利用率,还能减少向云端传输大量数据的必要性,这对于提高雷达数据处理的及时性和效率至关重要。为满足这一需求,本文提出了一种将时空注意力模块(STAM)与长短期记忆门控循环单元(ST-ConvLSGRU)集成的模型,以提高雷达回波预测的准确性,同时充分利用 MEC 的优势。STAM 通过扩展预测单元的时空感受野,有效捕捉了关键的帧间运动信息,同时对卷积结构和损失函数进行了优化,进一步提高了模型的预测性能。实验结果表明,我们的方法显著提高了移动边缘计算环境中短期天气预报的准确性,为在资源有限的动态条件下处理雷达回波数据提供了高效实用的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting
With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.
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